NSF Award
2323730
Non-technical Description: Polymer materials such as thermoplastics, thermosets, elastomers, and gels, were produced on a massive scale of 367 million tons globally in 2020. However, due to their construction at the molecular level, current polymer designs must strike a balance between being hard, durable, and easy to shape or mold. Recent advancements in polymer design take advantage of more flexible molecular connections to open up opportunities for more robust, long-lasting materials. Despite these achievements, making polymers with advanced properties remains a grand challenge because current designs largely depend on the researcher's intuition, and there is limited understanding of how the structure of a polymer determines its properties and how easy it is to process. This collaborative project seeks to use a systematic, data-driven approach to overcome these challenges by developing polymers with adaptive molecular structures that can withstand extreme conditions where they must survive exposure to large mechanical forces and repair themselves when damaged. This research aims to establish a comprehensive, accelerated materials discovery loop that includes multiscale computational simulations, rapid polymer synthesis, automated fabrication with tandem mechanical characterization, and machine learning-guided design. This project aligns with the objectives of the Materials Genome Initiative, using automation, simulations, rapid synthesis, machine learning, and 3D printing to speed up the design and discovery of high-performance polymers. The successful outcome of this collaboration will lead to the creation of polymers with unprecedented mechanical properties and processibility that are suitable for producing wearable sensors, soft actuators, and energy harvesting devices and be compatible with future manufacturing processes.<br/><br/>Technical Description: This DMREF project endeavors to create an integrated experimental and computational database of adaptive polymer networks, showcasing exceptional stretchability, high resilience, and impressive self-healing abilities. The primary focus lies in the development of novel double-threaded slide-ring polymers, macromolecules with dynamic covalent chemical linkages, and double networks with both. The aim is to transcend the conventional rigidity-toughness-processibility paradigm in polymer design and weave this into an automated discovery loop to explore new areas of materials space. High-throughput synthesis and automated experiments will accelerate the discovery of polymers and be informed by molecular dynamics simulations along the way. The data generated from these experiments will be harnessed to refine a machine learning-based active learning process for property optimization. Moreover, the project introduces 3D printing into the design workflow as a unique platform to validate mechanical properties while refining their manufacturability. This collaborative research overall aspires to deliver several key advancements: (1) pioneering the use of double-threaded polymers to fabricate slide-ring polymers for enhanced stretchability; (2) integrating dynamic covalent polymer design with extrusion-based 3D printing to enhance self-healing properties from the nano-to-macroscales; and (3) realizing high toughness, high rigidity, and high processibility via double network polymers construction using both slide-ring and dynamic covalent polymers. This project creates cross-discipline learning opportunities to enrich the research experiences of K-12, undergraduate, and graduate students, including a freely accessible online "polymer design playlist."<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
